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Exploratory Data Analysis with Python

  • Course Code: Data Analysis / BI - Exploratory Data Analysis with Python
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 4 Days Audience: This course is geared for Python experienced developers, analysts or others who wants to use Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas.

Course Snapshot 

  • Duration: 4 days 
  • Skill-level: Foundation-level Exploratory Data Analysis with Python skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who want to use 
  • Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas.  
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Exploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This course will help you gain practical knowledge of the main pillars of EDA – data cleaning, data preparation, data exploration, and data visualization. You’ll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You’ll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you’ll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence. By the end of this EDA course, you’ll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes 

Working in a hands-on learning environment, led by our Data Analysis with Python expert instructor, students will learn about and explore: 

  • Understand the fundamental concepts of exploratory data analysis using Python 
  • Find missing values in your data and identify the correlation between different variables 
  • Practice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python package 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Import, clean, and explore data to perform preliminary analysis using powerful Python packages 
  • Identify and transform erroneous data using different data wrangling techniques 
  • Explore the use of multiple regression to describe non-linear relationships 
  • Discover hypothesis testing and explore techniques of time-series analysis 
  • Understand and interpret results obtained from graphical analysis 
  • Build, train, and optimize predictive models to estimate results 
  • Perform complex EDA techniques on open source datasets 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas 

Pre-Requisites:  Students should have  

  • developers with some knowledge of Python.  
  • experienced with spreadsheet software who know the basics of Python. 

Course Agenda / Topics 

  1. Section 1: The Fundamentals of EDA 
  • Section 1: The Fundamentals of EDA 
  1. Exploratory Data Analysis Fundamentals 
  • Exploratory Data Analysis Fundamentals 
  • Understanding data science 
  • The significance of EDA 
  • Making sense of data 
  • Comparing EDA with classical and Bayesian analysis 
  • Software tools available for EDA 
  • Getting started with EDA 
  1. Visual Aids for EDA 
  • Visual Aids for EDA 
  • Technical requirements 
  • Line chart 
  • Bar charts 
  • Scatter plot 
  • Area plot and stacked plot 
  • Pie chart 
  • Table chart 
  • Polar chart 
  • Histogram 
  • Lollipop chart 
  • Choosing the best chart 
  • Other libraries to explore 
  1. EDA with Personal Email 
  • EDA with Personal Email 
  • Technical requirements 
  • Loading the dataset 
  • Data transformation 
  • Data analysis 
  1. Data Transformation 
  • Data Transformation 
  • Technical requirements 
  • Background 
  • Merging database-style dataframes 
  • Transformation techniques 
  • Benefits of data transformation 
  1. Section 2: Descriptive Statistics 
  • Section 2: Descriptive Statistics 
  1. Descriptive Statistics 
  • Descriptive Statistics 
  • Technical requirements 
  • Understanding statistics 
  • Measures of central tendency 
  • Measures of dispersion 
  1. Grouping Datasets 
  • Grouping Datasets 
  • Technical requirements 
  • Understanding groupby()  
  • Groupby mechanics 
  • Data aggregation 
  • Pivot tables and cross-tabulations 
  1. Correlation 
  • Correlation 
  • Technical requirements 
  • Introducing correlation 
  • Types of analysis 
  • Discussing multivariate analysis using the Titanic dataset 
  • Outlining Simpson’s paradox 
  • Correlation does not imply causation 
  1. Time Series Analysis 
  • Time Series Analysis 
  • Technical requirements 
  • Understanding the time series dataset 
  • TSA with Open Power System Data 
  1. Section 3: Model Development and Evaluation 
  • Section 3: Model Development and Evaluation 
  1. Hypothesis Testing and Regression 
  • Hypothesis Testing and Regression 
  • Technical requirements 
  • Hypothesis testing 
  • p-hacking 
  • Understanding regression 
  • Model development and evaluation 
  1. Model Development and Evaluation 
  • Model Development and Evaluation 
  • Technical requirements 
  • Types of machine learning 
  • Understanding supervised learning 
  • Understanding unsupervised learning 
  • Understanding reinforcement learning 
  • Unified machine learning workflow 
  1. EDA on Wine Quality Data Analysis 
  • EDA on Wine Quality Data Analysis 
  • Technical requirements 
  • Disclosing the wine quality dataset 
  • Analyzing red wine 
  • Analyzing white wine 
  • Model development and evaluation 
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